Population sizes, site fidelity and residence patterns of Australian ...

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Population sizes, site fidelity and residence patternsof Australian snubfin and Indo-Pacific humpbackdolphins: Implications for conservation

Guido J. Parraa,b,*, Peter J. Corkerona,c, Helene Marsha,b

aSchool of Tropical Environment Studies and Geography, James Cook University, Townsville 4811, Qld., AustraliabCRC Reef Research Centre, P.O. Box 772, Townsville 4810, Qld., AustraliacBioacoustics Research Program, Cornell Laboratory of Ornithology, 159 Sapsucker Woods Rd, Ithaca, NY 14850, USA

A R T I C L E I N F O

Article history:

Received 24 August 2005

Received in revised form

19 October 2005

Accepted 20 October 2005

Available online 1 December 2005

Keywords:

Conservation

Marine mammals

Population size

Movement patterns

Orcaella heinsohni

Sousa chinensis

0006-3207/$ - see front matter � 2005 Elsevidoi:10.1016/j.biocon.2005.10.031

* Corresponding author: Address: School of TAustralia. Tel.: +61 74 781 5824; fax: +61 7 47

E-mail address: [email protected]

A B S T R A C T

Very little is known about the ecology of snubfin Orcaella heinsohni and Indo-Pacific hump-

back dolphins Sousa chinensis in Australian waters. We used photo-identification data col-

lected between 1999 and 2002 in Cleveland Bay, northeast Queensland, to estimate

abundance, site fidelity and residence patterns of these species in order to make recom-

mendations for their effective conservation and management. Our abundance estimates

indicate that less than a hundred individuals of each species inhabit this coastal area. Even

with relatively unbiased and precise abundance estimates population trends will be extre-

mely difficult to detect in less than three years unless changes in population size are very

high (>20% p.a.). Though both species are not permanent residents in Cleveland Bay, they

both used the area regularly from year to year following a model of emigration and reim-

migration. Individuals of both species spend periods of days to a month or more in coastal

waters of Cleveland Bay before leaving, and periods of over a month outside the study area

before entering the bay again. Because of their small population sizes and movement pat-

terns, snubfin and humpback dolphins are particularly vulnerable to local extinction. Our

results illustrate that: (1) detection of population trends should not be a necessary criterion

for enacting conservation measures of both species in this region, and (2) efforts to main-

tain viable populations of both species in Cleveland Bay must include management strat-

egies that integrate anthropogenic activities in surrounding areas.

� 2005 Elsevier Ltd. All rights reserved.

1. Introduction

Coastal areas are among the marine habitats most at risk

from human activities (McIntyre, 1999; Moore, 1999). Conse-

quently, coastal dolphins are among the most threatened spe-

cies of cetaceans and most in need of management

intervention to reduce anthropogenic threats (Thompson

et al., 2000; DeMaster et al., 2001). Estimates of population size

er Ltd. All rights reserved

ropical Environment Stud81 4020.du.au (G.J. Parra).

and movement patterns are integral components of the infor-

mation needed to manage human impacts on wild cetaceans

(Hooker et al., 1999; Wilson et al., 1999; Ingram and Rogan,

2002; Hastie et al., 2003).

The conservation status of snubfin dolphins Orcaella hein-

sohni, formerly known as Irrawaddy dolphins Orcaella breviros-

tris and Indo-Pacific humpback dolphins Sousa chinensis

(hereafter humpback dolphins) in Australian waters is

.

ies and Geography, James Cook University, Townsville 4811, Qld.,

mailto:[email protected]

168 B I O L O G I C A L C O N S E R VAT I O N 1 2 9 ( 2 0 0 6 ) 1 6 7 –1 8 0

unknown, largely because of lack of research on either spe-

cies (Corkeron et al., 1997; Parra et al., 2002, 2004). This defi-

ciency hampers conservation and management efforts and

our ability to assess the impact of human activities on local

populations of this species. In addition, the latest efforts to

resolve the taxonomy of the members of the genera Orcaella

indicate that snubfin dolphins are endemic to the Austra-

lian/Papua New Guinea region (Beasley et al., 2005). Likewise,

species-level taxonomy of the genus Sousa remains in dispute

(Jefferson and Karczmarski, 2001; Jefferson, 2004). As both

species may potentially be endemic to Australian waters,

their conservation value is high and the estimation of most

basic population parameters (e.g., population size) is particu-

larly significant and urgent.

Obtaining accurate and precise estimates of the abun-

dance of cetaceans is usually difficult, expensive and time

consuming (Gerrodette, 1987; Taylor and Gerrodette, 1993).

Sampling and environmental variability affect our ability to

accurately estimate cetacean populations sizes and trends

(Taylor and Gerrodette, 1993; Forney, 2000; Thompson et al.,

2000). However, relatively precise and unbiased estimates of

population size can result from careful survey design and

consideration of the assumptions inherent in estimation

methods (Wilson et al., 1999; Read et al., 2003). Additionally,

power analysis can quantify the ability of monitoring pro-

grams to detect population trends (Gerrodette, 1987; Taylor

and Gerrodette, 1993).

Studying the movement patterns of cetaceans is also diffi-

cult, mainly because they spend most of their lives in an

underwater environment. Detailed studies of marine mam-

mal movements generally depend on remote tracking (Martin

and DaSilva, 1998; Kochman et al., 2003; Austin et al., 2004).

Fig. 1 – Map of Cleveland Bay Dugong Protected Area (DP

However, opportunistic identification of individual animals

provides data that can be used to produce realistic movement

models (Whitehead, 2001). Snubfin and humpback dolphins

are reliably identified from marks and pigmentation patterns

on their dorsal fins (Corkeron, 1990; Parra and Corkeron,

2001), making this technique appropriate for the study of

their movement patterns.

Here we present data on population sizes, site fidelity and

residence patterns of snubfin and humpback dolphins in

coastal waters of Cleveland Bay, adjacent to the city of Town-

ville in the Great Barrier Reef World Heritage Area, northeast

Queensland, Australia. Our purpose is to inform and improve

the design of conservation and management interventions

towards these species in Australian waters.

2. Methods

2.1. Data collection

Boat-based surveys were conducted in the coastal waters of

Cleveland Bay Dugong Protected Area (hereafter Cleveland

Bay, Fig. 1) from January 1999 to October 2002. The study area

extended 5–6 km offshore covering an approximate area of

310 km2 from Cape Cleveland to Black River mouth (Fig. 1).

Surveys were conducted at a steady speed of 10–12 km/h from

a 4.7 m rigid-hulled inflatable boat, powered by a 50-hp out-

board engine. Three observers searched for dolphins (one on

each side of the boat and one ahead) with naked eye and

7 · 50 binoculars. All surveyswere conducted in calm sea con-

ditions (i.e., Beaufort sea state 6 3 and swell 6 1 m) between

0600 and 1400 h. For survey purposes, the study area was

divided into four sections (A–D) of similar lengths (Fig. 1).

A) indicating survey route (—) and limits of DPA (—).

B I O L O G I C A L C O N S E R VAT I O N 1 2 9 ( 2 0 0 6 ) 1 6 7 –1 8 0 169

Surveys followed a predetermined route from Townsville Har-

bour to Black River mouth (covering sections A and B) or to

Cape Cleveland and back (covering sections C and D).

We defined a school as dolphins with relatively close spa-

tial cohesion (i.e., each member within 100 m of any other

member) that were involved in similar (often the same)

behavioural activities (modified from Connor et al., 1998).

Once a dolphin school was sighted, it was approached slowly,

to within 10 m, to record its location, identify the species,

estimate school size, assess the age composition of the

school, and take photographs of individual animals for

photo-identification. To define the age composition of a

school, three age classes were distinguished based on behav-

ioural cues and visual assessment using the average adult

size for each species as a reference: (1) adults: individuals

about 2–3 m long; (2) juveniles: individuals approximately 2/

3 the length of an adult, usually swimming in association

with an adult, but sometimes swimming independently; (3)

calves: individuals with light brown (snubfin dolphins) or light

grey (humpback dolphins) skin colour, 61/2 the length of an

adult, in close association with an adult, and swimming reg-

ularly besides or slightly behind an adult.

Photographs of individuals were taken as perpendicular to

the dolphin�s body axis as possible and concentrated mainly

on the dorsal fin. All photographs taken on surveys were

examined and classified into three grades (excellent, good,

and poor) according to focus, contrast between dorsal fin

and background, relative angle to the animal, and the size

of dorsal fin relative to the frame. Photographs classified as

excellent and good were used to identify individuals and de-

0

01

02

03

04

05

06

07

Jan

Feb

Mar

Apr

May Jun

Jul

Aug Sep Oct

Nov Jan

Mar

Apr

May Jun

Jul

Aug

91 99 2000

oM htn s fo

Cum

ulat

ive

num

ber

of d

olph

ins

iden

tifie

d

Fig. 2 – Discovery curves of the cumulative number of snubfin an

October 2002 in Cleveland Bay. The bars represent the number o

velop identification catalogues for each species (Wursig and

Jefferson, 1990).

2.2. Data selection

Most fieldwork effort took place during the dry season (May–

Nov). Nearly all identified dolphins of both species (snubfin:

98%; humpback: 94%) were captured during these months

(Fig. 2), thus our analysis of capture–recapture data was lim-

ited to this season. To obtain adequate sample sizes, capture

histories of each individual dolphin were pooled by year (i.e.,

if an animal was photographed at least once during May–Nov

it was considered captured for that year), resulting in four

sampling occasions (1999, 2000, 2001, and 2002).

2.3. Estimating population size

We defined the term �population� for both snubfin and hump-

back dolphins as the number of individuals of each species

frequenting the study area (Begon et al., 1996; Williams

et al., 2002) and used the terms abundance and population

size synonymously. Population sizes of snubfin and hump-

back dolphins were estimated using Schwarz and Arnason�s

parameterization of the Jolly-Seber open population model

(Schwarz and Arnason, 1996). This model provides abundance

estimates while allowing entries (i.e., births, immigration)

and losses (i.e., death, permanent emigration) in the popula-

tion under study and is suitable for long-term studies where

the use of models assuming population closure is not

reasonable.

Sep Oct

Nov Jan

Mar

Apr

May Jun

Jul

Aug Sep Oct

May Jun

Jul

Aug Sep Oct

2001 2 00 2

st du y

0

5

01

51

02

52

03

53

04

Sur

vey

effo

rt (

hrs)

Su evr y e troff

nS bu nif d o snihpl

uH pm ab kc d o pl snih

d humpback dolphins identified between January 1999 and

f survey hours spent in the field during each month of study.

Table 1 – Abundance estimates of (a) snubfin and (b) humpback dolphins in Cleveland Bay between January 1999 andOctober 2002

Jolly-Seber modelsa Marked animals Total population Model selection

Year n p SE N SE CV 95% CI h Ntotalb SE CV 95% CI np AICc DAICc

(a) Snubfin dolphins

(/t,pt) 1999 32 na na na na na na 0.78 na na na na 8 171.8 1.9

2000 43 0.9 0.07 48 4.5 0.09 40–58 0.70 69 7.0 0.10 57–84

2001 28 0.6 0.10 45 6.1 0.14 35–59 0.67 68 9.5 0.14 51–89

2002 32 na na na na na na 0.63 na na na na

(/t,p•) 1999 32 0.7 0.06 na na na na 0.78 na na na na 7 174.3 4.4

2000 43 54 4.5 0.08 46–64 0.70 78 7.1 0.09 65–93

2001 28 42 5.0 0.12 33–53 0.67 62 7.8 0.13 49–80

2002 32 43 6.5 0.15 32–58 0.63 69 10.9 0.16 51–94

(/•,pt) 1999 32 na na na na na na 0.78 na na na na 7 169.9 0.0

2000 43 0.9 0.07 49 4.3 0.09 41–58 0.70 70 6.7 0.10 58–84

2001 28 0.6 0.09 44 5.4 0.12 35–56 0.67 66 8.5 0.13 52–85

2002 32 0.8 0.13 43 7.2 0.17 31–59 0.63 68 11.9 0.17 48–95

(/•,p•) 1999 32 0.7 0.06 na na na na 0.78 na na na na 5 170.8 0.9

2000 43 53 3.6 0.07 46–60 0.70 76 6.0 0.08 65–88

2001 28 43 4.6 0.11 35–53 0.67 64 7.4 0.11 51–80

2002 32 42 5.6 0.13 32–54 0.63 67 9.4 0.14 51–88

(b) Humpback dolphins

(/t,pt) 1999 20 na na na na na na 0.77 na na na na 8 155.9 1.8

2000 25 0.8 0.13 32 5.9 0.18 23–46 0.66 49 9.1 0.19 34–70

2001 13 0.5 0.13 26 6.1 0.23 17–41 0.79 34 7.8 0.23 21–53

2002 30 na na na na na na 0.77 na na na na

(/t,p•) 1999 20 0.7 0.08 na na na na 0.77 na na na na 7 156.8 2.6

2000 25 35 5.3 0.15 26–47 0.66 53 8.4 0.16 39–72

2001 13 24 4.9 0.20 16–35 0.79 30 6.2 0.21 20–45

2002 30 45 6.5 0.14 34–60 0.77 59 8.7 0.15 44–78

(/•,pt) 1999 20 0.7 0.10 na na na na 0.77 na na na na 7 154.1 0.0

2000 25 0.7 0.10 34 4.1 0.12 27–43 0.66 51 6.6 0.13 40–66

2001 13 0.5 0.13 25 5.2 0.21 17–38 0.79 32 6.7 0.21 21–48

2002 30 0.8 0.23 35 9.3 0.27 21–59 0.77 46 12.2 0.27 27–77

(/•,p•) 1999 20 0.7 0.09 na na na na 0.77 na na na na 5 155.1 1.0

2000 25 34 4.5 0.13 27–44 0.66 52 7.1 0.14 40–68

2001 13 27 4.9 0.18 19–38 0.79 34 6.3 0.19 24–49

2002 30 42 7.3 0.18 30–59 0.77 54 9.6 0.18 38–77

The model that best fitted the data of both species according to the Akaike Information Criterion corrected for small sample sizes (AICc) was

model (/•,pt). Models (/t,pt) and (/•,p•) also provided good fit to the data (i.e., DAICc scores within 2 units of best model) of both species.

Following the parsimony principle, the model (/•,p•) was selected as the most appropriate (in bold italics) because it has fewest parameters.

a Model notation follows Lebreton et al. (1992): / = survival probability; p = capture probability; t = time dependent effect; and • = constant

effect. Other notations: n = number of animals captured; p = capture probability; N = estimate of number of marked animals; SE = standard

error; CV = coefficient of variation; CI = confidence interval; h = proportion of identifiable animals; Ntotal = estimate of total population size after

correcting for proportion of identifiable individuals; np = number of estimable parameters in model; DAICc = difference between AICc and

minimum AICc obtained; and na = not available.

b The total population size (Ntotal) of snubfin and humpback dolphins in Cleveland Bay and its variance (Var(Ntotal)) were derived as (Williams

et al., 1993; Wilson et al., 1999; Chilvers and Corkeron, 2003):

Ntotal ¼ Nh ; VarðNtotalÞ ¼ N2

totalvarNN2 þ 1�h

nh

� �:


Four conditional forms of the Jolly-Seber model were fit-

ted to the data (Table 1). The parameters of each model were

estimated using maximum likelihood estimation using the

computer program POPAN-5 (Schwarz and Arnason, 1996;

Arnason et al., 1998). The appropriate model for inference

was selected using the Akaike Information Criterion cor-

rected for small-sample sizes (AICc) (Burnham and Ander-

son, 1998). Models differing by less than two units from

the model with minimum AICc (DAICc) also provide good

descriptions of the data (Burnham and Anderson, 1998).

When more than one model provided a good description of

the data, we followed the principle of parsimony and se-

lected the model with lower number of parameters as the

most appropriate.


2.4. Total population size

Our abundance estimates from Jolly-Seber models pertain

only to the population of marked animals. The total popula-

tion size (and its variance) of snubfin and humpback dolphins

in Cleveland Bay were estimated by taking into account the

proportion of identifiable individuals of each species (Wil-

liams et al., 1993; Wilson et al., 1999; Chilvers and Corkeron,

2003). The proportion of identifiable individuals within the

population for each year was estimated as the number of

excellent and good quality photographs showing a recognis-

able individual from a random sample of 300 photographs

from each species.

2.5. Validation of model assumptions

The estimation of demographic parameters under Jolly-Seber

capture–recapture models requires several assumptions; the

violation of which can lead to bias in population estimates

(Table 2). We used information on the biology of these two

Table 2 – Validation of the assumptions involved in Jolly-Sebepopulation sizes of snubfin and humpback dolphins in Clevel

Assumption Bias in estimates

Mark recognition

and mark loss

Upwards (1) Used only good a

individuals.

(2) Analysis restricted

(3) Regular sampling

monitoring of ma

(4) Additional marks

shape) to notches

for individual iden

(5) Only one experien

photographs ensu

uals and grading o

Homogeneous

capture and

survival

probabilities

Downwards (1) The pooled v2 st

assumptions of h

ties were not v

p = 0.135; humpba

(2) Average capture p

species were relat

No behavioural

responses

Trap shy = upwards (1) With photo-identi

stress induced by

researcher.

(2) Pradel�s test for tra

happy’’ or ‘‘trap-sh

dolphins: Z = 0.0;

df = 4; p = 0.497).

Trap happy =

downwards

Permanent

emigration

Direction of bias

depends on the

nature of the

emigration process

(1) Estimates of the c

tively high (see Ta

(2) No indication of

pooled v2 statistic

Instantaneous

sampling

Upwards Sampling occasions s

duration (6–7 months

(decades).

a The pooled v2 statistics (Test 2 + Test 3) for homogeneity in capture a

carried out using the program U-Care (Choquet et al., 2002).

species and goodness-of-fit tests to evaluate potential viola-

tions of population analyses. We considered all assumptions

to be valid (Table 2).

2.6. Analysing the power to detect population trends

We used Gerrodette�s (1987) inequality model to investigate

the ability of a series of population estimates to detect popu-

lation trends. The probability of Type I and II errors was 0.05

as this is the standard level of a and b used to claim a statis-

tically significant effect, and high statistical power

(Power = 1 � b = 0.95). We used the range of CV values ob-

tained from the population estimates to investigate the time

it will take to detect different rates of population change by

conducting annual surveys.

2.7. Site fidelity

Although some animals were identified more than once dur-

ing the same day, the first sighting of the day, and only

r capture–recapture models used for the estimation ofand Bay

Validationa References

nd excellent quality photographs to identify

to individuals with long-lasting marks.

over four years permitted comprehensive

rked animals.

(i.e., white pigmentation patterns, dorsal fin

and scars in dorsal fin were also considered

tification.

ced person was responsible for cataloguing

ring consistency in the recognition of individ-

f photographs.

Pollock et al.

(1990), Williams

et al. (2002)

atistics (Test 2 + Test 3) indicated that the

omogeneous capture and survival probabili-

iolated (snubfin dolphins: v2 = 7.0; df = 4;

ck dolphins: v2 = 7.94; df = 4; p = 0.094).

robabilities obtained in this study for both

ively high (>0.5, see Table 1).

Burnham et al.

(1987), Pollock

et al. (1990),

Williams et al.

(2002)

fication techniques animals are not subject to

capture, handling, or physical marking by the

p-dependence showed no indication of ‘‘trap-

y’’ behaviour by marked individuals (snubfin

df = 4; p = 1; humpback dolphins: Z = 0.67;

Pollock et al.

(1990), Pradel

(1993), Williams

et al. (2002)

apture probabilities of both species were rela-

ble 1).

heterogeneity in capture probabilities (see

s of Test 2 + Test 3 above).

Kendall (1997),

Williams et al.

(2002)

elected for analysis were relatively short in

) in comparison with the dolphins� lifespan

Pollock et al.

(1990), Williams

et al. (2002)

nd survival probabilities and Pradel�s test for trap-dependence were


sightings separated at least a day apart were used in the

analysis of site fidelity and residence patterns to minimise

likelihood of dependence in the data. To investigate the pres-

ence of identified individuals in the study area over time, we

calculated: (1) the number of months a dolphin was identi-

fied as a proportion of the total number of months in which

at least one survey was conducted (i.e., monthly sighting

rate) and (2) the number of calendar years a dolphin was

identified as a proportion of the total surveyed (i.e., yearly

sighting rate). Potentially, monthly sighting rates range

between 0.02 (i.e., animals sighted in only one month out

of 36) and one for an individual sighted in all months.

Similarly, potential yearly sighting rates ranged from 0.25

(i.e., animals sighted in only one year out of four) and one

for an individual sighted in all four years of study.

We used the CrimeStat spatial statistics software to mea-

sure the standard distance deviation (SXY) to investigate if

individual dolphins displayed site fidelity towards specific

areas within Cleveland Bay. The standard distance deviation

is the spatial equivalent to the standard deviation (Levine,

2002). The SXY measures the standard deviation of the dis-

tance of the location of each individual from their mean

centre:

SXY ¼

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiPXi � X� �2 þP

Yi � Y� �2

N� 2

s;

where Xi and Yi are the coordinates of the location of each

individual dolphin (in metres), X and Y are the means of

each coordinate and N is the total number of times an indi-

vidual animal was sighted. The more dispersed individual

locations are, the larger the standard distance deviation

and the less faithful an individual was to a specific area

within Cleveland Bay. To provide a balance between the rep-

resentativeness of the data (i.e., include the maximum

number of individuals) and its reliability (e.g., include indi-

viduals with maximum sighting frequencies, Chilvers and

Corkeron, 2002), SXY was calculated only for individuals that

were observed on at least eight occasions, separated at least

one day apart and with at least one of those occasions sep-

arated by a year.

2.8. Residence times

To estimate the amount of time identified individuals reside

inside Cleveland Bay, we calculated the probability that if an

individual is identified in the study area at any time, it is iden-

tified during any single identification made in the area some

time lag later (i.e., lagged identification rate, Whitehead,

2001). Plots of lagged identification rates against time were

produced for all identified individual dolphins of each species

as these plots provided indications of the temporal use of the

area by individual animals. A plot of lagged identification

rates that drops sharply after a certain time lag and then lev-

els off above zero after a larger time lag indicates that: (1)

many animals leave the study area after residing in the area

for a certain time lag, (2) some animals remain resident,

and/or (3) other animals reimmigrate into the study area

(Whitehead, 2001).

After estimating lagged identification rates for each spe-

cies, we compared the observed rates to expected lagged

identification rates from exponential mathematical models

of emigration/mortality and emigration + reimmigration

(Whitehead, 2001). The model minimising the adjusted

Akaike Information Criterion for small-sample bias (AICc)

was chosen as the best fit model (Burnham and Anderson,

1998). Computation of lagged identification rates and model

fitting was carried out using the computer software SOCPROG

2.1 (Whitehead, 2004).

3. Results

3.1. Photo-identification and proportion of animalsidentifiable

Between 1999 and 2002 a total of 117 schools of snubfin dol-

phins and 143 of humpback dolphins were sighted in Cleve-

land Bay, from which 63 snubfin and 54 humpback dolphins

were identified. All identified animals were adults, with the

exception of one juvenile snubfin dolphin. Overall, the

cumulative number of identified individuals (i.e., rate of dis-

covery) of both species did not decrease with time, suggest-

ing populations were open for the duration of the study and/

or unrecognisable animals acquired new marks as our study

progressed (Fig. 2). We considered the latter unlikely as the

new animals been added to the catalogue displayed old

marks instead of fresh new marks. The rate of discovery of

new individuals was not steep with an average of 1.7 ± 0.40

(±SE) snubfin and 1.5 ± 0.35 humpback dolphins added to

the catalogue per month. By the end of 2000, 84% of the rec-

ognisable snubfin and 72% of humpback dolphins had been

identified.

The analysis of random photographs of excellent and

good quality for each year indicated that the proportion of

snubfin and humpback dolphins that could be reliably iden-

tified from the population were high (Table 1). The propor-

tion of identifiable snubfin and humpback dolphins varied

from 0.63 to 0.78 and from 0.66 to 0.79, respectively, depend-

ing on the year.

3.2. Population size of marked animals and modelselection

Abundance estimates of marked animals (N) from the four

Jolly-Seber models fitted to the data are presented in Table

1. For each year where a comparison is available, abundance

estimates of marked animals for both species did not vary

greatly between models. The model that best fitted the data

for snubfin and humpback dolphins was the model in which

capture probabilities vary with time and survival probabilities

were constant (/•,pt, Table 1). The full time dependent model

(/t,pt) and the constant model (/•,p•) also provided good fit to

the data (i.e., DAICc scores within 2 units of best model) of

both species, with similar estimates of N to the best model

(Table 1).

Following the principle of parsimony, we selected as best

model for both species, the constant capture-constant sur-

vival model (/•,p•) as it has the lowest number of parameters.

Estimates of N from the constant capture-constant survival

model varied from 42 to 53 marked snubfin dolphins and from

27 to 41 marked humpback dolphins (Table 1).

Fig. 3 – Relationships between different rates of population

change, time until trend detection, and coefficient of

variation (CV) for annual population estimates. The CVs

used to present data variability are the values obtained for

population estimates of snubfin and humpback dolphins.

The probabilities of Type I (a ) and Type II (b ) errors were set

at 0.05.


3.3. Total population size

The total number of snubfin and humpback dolphins using

the study area was less than 100 individuals for each spe-

cies (Table 1). The total population size for snubfin dolphins

ranged from 64 individuals in 2001 to 76 in 2000 (Table 1a),

Table 3 – Effect of different annual rates of population change oof snubfin and humpback dolphins with yearly survey interva

Species CV Rate ofchange (r)

Number ofsurveys

required (n)

Numbeyears to de

(t(n � 1

Snubfin 0.08 0.05 7 6

0.1 5 4

0.15 4 3

0.2 3 2

Humpback 0.14 0.05 11 10

0.1 7 6

0.15 5 4

0.2 4 3

Data variability is specified at CV = 0.08 for snubfin dolphins and 0.14 fo

precision obtained for the abundance estimates of snubfin and humpbac

was set at the 0.05 level.

a Estimates were derived from:

r2n3 P 12CV2ðZa=2 þ ZbÞ2;

where r is the annual rate of population change, n is the number of pop

total population size, Za/2 is the one-tailed probability of making a Type

probability of Type I and II errors was 0.05 as this is the standard level

statistical power (Power = 1�b = 0.95).

and from 34 humpback dolphins in 2001 to 54 in 2002 (Ta-

ble 1b).

3.4. Ability to detect population trends

The time required to detect a population trend in either spe-

cies by carrying annual surveys decreases with increasing

rates of population change (Fig. 3). With the highest level of

precision obtained for the abundance estimates of snubfin

dolphins (CV = 0.08), we estimated that it will take six years

to detect a population change of 5% p.a., but two years to de-

tect a 20% p.a. change (Table 3). The total percentage change

in the population of either species that will have occurred

by the time a 5% or 20% p.a. increase or decrease is detected

is high (Table 3). For example, a population of 76 snubfin dol-

phins (CV = 0.08) decreasing at 5% per year, would consist of

only 55 individuals by the time such trend was detected. If

the rate of decline was 20% per year, only 49 individuals

would remain by the time the trend was detected. For a pop-

ulation of 52 humpback dolphins (CV = 0.14) estimates are 32

and 25 respectively.

3.5. Site fidelity

Twelve snubfin dolphins (19%) and 22 (41%) humpback dol-

phins were identified only once throughout the study period

(Fig. 4a). However, 68% of snubfin and 52% of humpback dol-

phins were identified in more than one calendar year (Fig. 4b).

Relative to the total number of months surveyed, most

snubfin dolphins identified were sighted relatively infre-

quently (mean ± SE = 0.12 ± 0.01 sightings per month) (Fig. 4c).

However, yearly sighting rates (0.54 ± 0.03 sightings per year)

n the number of years required to detect population trendsls (t = 1) following Gerrodette�s inequality model (1987)a

r oftection))

Total % changeat detection for

decreasing population(1 � r)(t(n�1)) � 1

Total % change attrend detection for

increasing population(1 + r)(t(n�1)) � 1

�0.28 0.36

�0.32 0.41

�0.34 0.43

�0.35 0.42

�0.39 0.60

�0.45 0.73

�0.49 0.78

�0.52 0.81

r humpback dolphins. These CVs correspond to the highest level of

k dolphins (see Table 1). The probability of Type I (a) and II (b) errors

ulation estimates, CV is the coefficient of variation of the estimated

I error (a) and Zb is the probability of making a Type II error (b). The

of a and b used to claim a statistically significant effect, and high

Fig. 4 – Sightings of 63 and 54 snubfin and humpback dolphins identified in Cleveland Bay between 1999 and 2002: (a) total

number of sightings of all identified individuals; (b) number of months and years in which each individual dolphin was

sighted; (c) number of months and years a dolphin was identified as the proportion of the total number of months and years

surveyed.


indicated that many of the snubfin dolphins identified were

seen in more than one calendar year. Humpback dolphins

showed a similar pattern, with low monthly sighting rates

(0.10 ± 0.02 sightings per month), and relatively high sightings

across years (0.46 ± 0.03 sightings per year) (Fig. 4c).

The standard deviation of the distance of each individual

dolphin location from their mean centre indicated that over

50% of the snubfin and humpback dolphins sighted on at least

eight occasions were found within less than 10 km of their

mean centre (Fig. 5).

Fig. 5 – Frequency distribution of the standard deviation of

the distance of each individual dolphin location from their

mean centre (i.e., standard distance deviation) for all

snubfin dolphins (n = 15) and humpback dolphins (n = 9)

identified P 8 times in Cleveland Bay between 1999 and

2002.


3.6. Residence times

The lagged identification rate of snubfin dolphins fall after

lags of approximately 3 to 30 days and then levelled off above

zero at longer time lags (Fig. 6a). This pattern suggests that

animals may spend periods of up to 30 days in the bay before

leaving the study area. The lagged identification rate of

humpback dolphins showed a similar pattern but with differ-

ent residence times. For humpback dolphins it appears that

animals left the study area after periods of 10–140 days

(Fig. 6b). For both species the lagged identification rate lev-

elled off above zero, suggesting that some animals are perma-

nent residents and/or others reimmigrate into the study area

after longer time lags.

Of the two models applied to the data, the model curve

of emigration and reimmigration into the study area fitted

the data best for both species (Fig. 6). This model also

showed the lowest AICc values (snubfin: 17841; humpback:

17146) in comparison to emigration/mortality models (snub-

fin: 17913; humpback: 17183). Estimates of mean population

size and residence times from this model indicate that

approximately 14 (14.3 ± SE = 1.77, 95% CI = 11.6, 18.5) snub-

fin dolphins and 10 (10.1 ± SE = 1.87, 95% CI = 7.7, 14.1)

humpback dolphins were in the study area at any one time,

and that animals could spend from a few days to over a

month inside the study area before leaving. Snubfin dol-

phins appeared to reside inside the study area for periods

of 30 days (30.3 ± SE = 24.70, 95% CI = 17.2, 56.2), and spend

periods of 48 days (47.8 ± SE = 29.7, 95% CI = 27.5, 85.3) out-

side the study area before entering back into it. Humpback

dolphins had considerably longer residence times inside

the study area of 141 days (±SE = 110.3, 95% CI = 88.7,

281.1), and periods of 109 days (±SE = 47.7, 95% CI = 68.1,

212.7) outside the study area.

4. Discussion

4.1. Estimates of abundance

Our results indicate that a small number of snubfin and

humpback dolphins inhabit the coastal waters of Cleveland

Bay. From detailed examination of the assumptions involved

in mark recapture analyses with open population models,

we were able to derive what we consider to be relatively

unbiased and precise abundance estimates for both species.

We estimated that substantially less than a hundred indi-

viduals of each species used the study area between 1999

and 2002.

With no previous estimates of abundance, it is impossible

to assess if populations of both species in Cleveland Bay have

been stable, increasing or decreasing. Analysis of data from

three aerial surveys along most of the eastern Queensland

coast between 1987 and 1995 suggest population(s) of hump-

back dolphins may be in decline (Corkeron et al., 1997). Local

populations of both species have been subject to anthropo-

genic mortality in the past (Heinsohn, 1979; Paterson, 1990).

We regard somemortality in commercial gillnets as inevitable

because nets are mainly set in waters close to the coast and

there is evidence that both genera are vulnerable to gillnet-

ting practices (Amir et al., 2002; Smith et al., 2003). Further-

more, incidental bycatch of these species has been

recognised, and discussed with the authors (PJC and HM), by

gillnetters elsewhere in Queensland.

4.2. Trends in abundance

The data collected in this study do not provide an insight into

the current trends of local abundance of snubfin and hump-

back dolphins. The analysis of statistical power of capture–

recapture methods indicate that population trends will be

extremely difficult to detect in less than three years, unless

changes in population size are very high (>20% p.a.). At such

high levels of annual change, local populations of snubfin

and humpback dolphins could have decreased to very low

levels by the time trend is detected.

The estimation of trends becomes more complex if we

take into account the apparent open nature of both popula-

tions. The initial increase in the discovery rate of new indi-

viduals of both species during 1999 (Fig. 2) is an attribute of

the beginning of the study. However, the alternating in-

creases and plateaus in the discovery curve later in the

study suggest a regular influx of new individuals to the

study area. Site fidelity and residence patterns of identified

individuals suggest that there is substantial movement of

animals out of the study area, but that a high proportion

tends to return following a model of emigration and reim-

migration. Two snubfin dolphins identified in this study

have been photo-identified in Bowling Green Bay, immedi-

ately south of Cleveland Bay (Parra and Corkeron, 2001). It

is therefore important that future survey coverage include

areas to the north and south of the study area to assess

population structure and how movement of individuals be-

tween these areas might affect abundance estimates at a lo-

cal level.

Fig. 6 – Lagged identification rates (�) for (a) adult snubfin dolphins and (b) humpback dolphins in coastal waters of Cleveland

Bay, together with the expected lagged identification rates and estimated standard errors (bars) from emigration and

reimmigration models fitted to the data using maximum likelihood.


4.3. Site fidelity and residence patterns

Most individual dolphins identified in Cleveland Bay do not

reside in the study area permanently, but use the study area

regularly from year to year. Modelling of sighting patterns

suggested that movement patterns of most individuals of

both species followed a model of emigration and reimmigra-

tion into the study area. Thus, the coastal waters of Cleveland


Bay appear to be an important part of the home ranges of

both snubfin and humpback dolphins in the region around

Townsville.

The large proportion of humpback dolphins observed only

once (41%) indicates that there is a high number of individu-

als that either die, or spend most of their time outside the

study area either in offshore or in adjacent waters to Cleve-

land Bay. In contrast, the smaller proportion of snubfin dol-

phins observed only once (22%) indicates that most

individuals use the coastal waters of Cleveland Bay. Eight

humpback dolphins and three snubfin dolphins were found

dead in the Townsville region and surrounding areas between

1999 and 2002, so some emigration may be mortality (Haynes

et al., 1999; Haynes and Limpus, 2000, 2002; Limpus et al.,

2003).

Although we expect both species also occur in offshore

waters of Cleveland Bay there is evidence that animals occur

mainly in waters close to the coast. Pilot studies in Cleveland

Bay totalling 14 h and 163 km of line transect that covered

waters up to 10 km from the coast yielded no sightings be-

yond 5 km (Parra unpublished data). Additionally most sight-

ings of snubfin and humpback dolphins made during aerial

surveys (Corkeron et al., 1997; Parra et al., 2002) and boat-

based line transect surveys including offshore waters (waters

>10 km from the coast) of different areas along the Queens-

land coast, occurred in waters within 6 km from the nearest

coastline (Corkeron et al., 1997; Parra et al., 2002; Parra,

2005). Thus, it is likely that the proportion of humpback dol-

phins and snubfin dolphins seen only once are occasional vis-

itors spending most of their time in coastal waters outside

Cleveland Bay.

The overall low standard distance deviations displayed by

frequently sighted individuals of both species suggest that

individuals repeatedly come back to particular areas within

the study area. This localised pattern is indicative of preferen-

tial use of some areas by individuals. These results corre-

spond with analysis of space use patterns of snubfin and

humpback dolphins at the population level in Cleveland Bay

(i.e., schools of dolphins instead of individuals were used for

analysis of space use), where it was shown that use of space

by both species was not random and schools tended to con-

centrate their activities in certain core areas (Parra, 2005).

The spatial and temporal variability in the quality of a site,

and breeding and foraging success (Switzer, 1997a,b; Irons,

1998) have been suggested to affect site fidelity. Cleveland

Bay is a large estuarine system receiving freshwater input

from several rivers and creeks and fish are abundant (Robert-

son and Duke, 1987, 1990). Foraging and socialising (i.e., socio-

sexual behaviour) are among the predominant behaviours ob-

served for snubfin and humpback dolphins in Cleveland Bay

(Parra, 2005). Thus, both habitat characteristics and species

behaviour suggest that individuals of both species may return

regularly to the study area because they have higher chances

of finding prey and/or mating conspecifics.

4.4. Implications for conservation

Small populations are more prone to extinction than large

stable populations because of loss of genetic variability and

environmental and demographic stochasticity (Caughley

and Gunn, 1996). Recent studies across many vertebrate taxa

suggest that the minimum size required for a population to

be viable (i.e., the smallest size a population can have to

have a 99% probability of persistence for 40 generations) in

the long-term is thousands to tens of thousands of individ-

uals (Reed et al., 2003). Population viability analysis of well

known coastal dolphin species (i.e., bottlenose dolphin,

Tursiops truncatus, and Hector�s dolphin, Cephalorhynchus

hectori) indicate that populations of less than a hundred ani-

mals face very high extinction probabilities (Thompson

et al., 2000; Burkhart and Slooten, 2003). Our low estimates

raise concerns about the long-term survival of both species

in this local region and emphasizes the need to increase

research and conservation efforts in Australia if conserva-

tion is to be successful.

Although it is difficult to be certain about the status of

snubfin and humpback dolphins in Queensland waters, our

results for Cleveland Bay indicate that, at least at a local scale,

populations of both of these species are small. Population

estimates at a regional level (e.g., Queensland) are likely to

be in the order of thousands rather than tens of thousands.

This conclusion is substantiated by: (1) the low numbers of

snubfin and humpback dolphins sighted during aerial surveys

covering most of the east Queensland Coast between 1987

and 1995 (i.e., 29 sightings of snubfin dolphins and 54 sight-

ings of humpback dolphins (Corkeron et al., 1997; Parra

et al., 2002); (2) the low number of sightings during boat-based

line transect surveys in selected areas of northeast Queens-

land (22 sightings of Irrawaddy dolphins and 14 sightings of

humpback dolphins, Parra, 2005); and (3) the low estimates

of abundance for humpback dolphins in Moreton Bay, an area

approximately four times the size of Cleveland Bay, (i.e., 119

individuals for the period of August 1985 to February 1987,

and 163 individuals for May 1984 to February 1986, Corkeron

et al., 1997).

The low population numbers of snubfin and humpback

dolphins and our inability to detect trends reinforce the asser-

tions that scientific proof of decline or increase should not be

a necessary criterion for enacting conservation measures

(Taylor and Gerrodette, 1993; Wilson et al., 1999; Thompson

et al., 2000). Against this background, the first priority of man-

agers should be to reduce and control all direct threats (e.g.,

gillnets, shark nets) to local populations while minimising

the impacts of management decisions on different stake-

holder groups.

The recurrent use of coastal waters of Cleveland Bay by

both species from year to year poses some concerns about

the long-term survival of local populations if current habitat

quality is not improved, or at least maintained. Species with

high levels of site fidelity are vulnerable to population de-

clines due to habitat degradation and loss, particularly when

those species occupy relatively restricted habitats (Warkentin

and Hernandez, 1996). The various habitats within the home

range of local populations of snubfin and humpback dolphins

are unlikely to be of the same quality. Consequently, degrada-

tion and loss of coastal habitats in Cleveland Bay can lead to

an increase in distance among habitable patches and/or

reduction in number of remnant habitats (i.e., habitat frag-

mentation; Andren, 1994). For example, a large scale loss of

seagrass habitat in Hervey Bay, Queensland, following a


cyclone and two floods resulted in unprecedented deaths and

decline of the local dugong population, Dugong dugon (Preen

and Marsh, 1995).

As individuals of both species spent considerable time out-

side the study area, management strategies aimed at conserv-

ing dolphins within Cleveland Bay must include human

activities in surrounding areas. The current level of protection

offered to snubfin and humpback dolphins in Cleveland Bay is

relatively good. Because of its status as a Dugong Protected

Area Type A since 1997, gillnetting is restricted in Cleveland

Bay. In addition, shark nets to protect bathers were replaced

with baited drumlines in 1992 and the number of dolphins

killed at a regional level due to the Queensland Shark Control

Program has declined (Gribble et al., 1998). However, adjacent

areas to Cleveland Bay offer different levels of protection

regarding mesh netting practices. For example, mesh netting

activities are allowed to continue with some safeguards and

restrictions in Bowling Green Bay, a Dugong Protected Area

Type B to the south of Cleveland Bay. In Halifax Bay, North

of Cleveland Bay, there are no regulations regarding mesh

netting practices per se. Although 38% of coastal waters

(i.e., waters within 10 km from the mainland) along the urban

coast of the Great Barrier Reef World Heritage Area are pro-

tected from gillnetting (Alana Grech, personal communica-

tion 2005), entanglement threats in waters adjacent to

Cleveland Bay pose some risk to the maintenance of local

populations.

The Townsville region is one of the largest growing coastal

areas in northeast Queensland (145,000 inhabitants) with an

average annual growth rate of 2–3% over the past 30 years

(King, 2003). Given the small population sizes of snubfin and

humpback dolphins estimated in Cleveland Bay, efforts to

maintain viable populations of both species will require

improvements to current levels of protection inside as well

as adjacent areas. Such protection must not only involve

the management of gillnetting activities, but of other human

activities adjacent to the coast that have been identified as

potentially threatening (pollution, vessel traffic, and overfish-

ing, Parra et al., 2002, 2004).

Although the preservation of suitable habitats is neces-

sary, the persistence of snubfin and humpback dolphins in

Australian waters cannot occur without an understanding

of their distribution, demography and population genetics at

local and regional levels. Future research in these areas will

improve our capacity to provide effective management ac-

tions towards the conservations of these two species.

Acknowledgements

Funding for this project was provided by Australia�s Natural

Heritage Trust, Sea World Research and Rescue Foundation,

the Cooperative Research Centre for the Great Barrier Reef

World Heritage Area (incorporated as CRC Reef Research Cen-

tre), and the PADI Foundation. G.J.P. was the recipient of a

scholarship from Colfuturo and an International Postgraduate

Research Scholarship from the Australian Government during

this study. We thank C.J. Schwarz, P. Arnold, A. Grech, I. Beas-

ley, D.A. Saunders, and two anonymous reviewers for helpful

comments that greatly improved the quality of this manu-

script.We thank all the volunteerswho participated in the col-

lection of field data for this study. Fieldwork was carried out

under permit from the Great Barrier Reef Marine Park Author-

ity and with ethics approval from James Cook University.

R E F E R E N C E S

Amir, O.A., Berggren, P., Jiddawi, N.S., 2002. The incidental catch ofdolphins in gillnet fisheries in Zanzibar, Tanzania. WesternIndian Ocean Journal of Marine Science 1, 155–162.

Andren, H., 1994. Effects of habitat fragmentation on birds andmammals in landscapes with different proportions of suitablehabitat: a review. Oikos 71, 355–366.

Arnason, A.N., Schwarz, C.J., Boyer, G., 1998. POPAN-5: a datamaintenance and analysis system for mark-recapture data,Scientific Report. Department of Computer Science, Universityof Manitoba Winnipeg, Manitoba, Canada.

Austin, D., Bowen,W.D., McMillan, J.I., 2004. Intraspecific variationin movement patterns: modelling individual behaviour in alarge marine predator. Oikos 105, 15–30.

Beasley, I., Robertson, K.M., Arnold, P., 2005. Description of a newdolphin, the Australian Snubfin dolphin Orcaella heinsohni sp.n. (Cetacea, Delphinidae). Marine Mammal Science 21,365–400.

Begon, M., Harper, J.L., Townsend, C.R., 1996. Ecology: Individuals,Populations and Communities, third ed. Blackwell Science,Oxford.

Burkhart, S.M., Slooten, E., 2003. Population viability analysis forHector�s dolphin (Cephalorhynchus hectori): a stochasticpopulation model for local populations. New Zealand Journalof Marine and Freshwater Research 37, 553–566.

Burnham, K.P., Anderson, D.R., 1998. Model Selection andInference: A Practical Information-Theoretic Approach.Springer-Verlag, New York.

Burnham, K.P., Anderson, D.R., White, G.C., Brownie, C., Pollock,K.H., 1987. Design and analysis methods for fish survivalexperiments based on release–recapture. American FisheriesSociety Monograph 5, Bethesda, Maryland.

Caughley, G., Gunn, A., 1996. Conservation Biology in Theory andPractice. Blackwell Science, Oxford, England. p. 445.

Chilvers, B.L., Corkeron, P.J., 2002. Association patterns ofbottlenose dolphins (Tursiops aduncus) off Point Lookout,Queensland, Australia. Canadian Journal of Zoology 80,973–979.

Chilvers, B.L., Corkeron, P.J., 2003. Abundance of Indo-Pacificbottlenose dolphins, Tursiops aduncus, off Point Lookout,Queensland, Australia. Marine Mammal Science 19, 85–95.

Choquet, R., Reboulet, A.M., Pradel, R., Gimenez, O., Lebreton,J.-D., 2002. U-Care (Utilities-Capture–Recapture) user�s guide,version 2.0, Mimeographed document, CEFE/CNRS,Montpellier (ftp://ftc.cefe.cnrs-mop.fr/biom/Soft-CR/).

Connor, R.C., Mann, J., Tyack, P.L., Whitehead, H., 1998. Socialevolution in toothed whales. Trends in Ecology and Evolution13, 228–232.

Corkeron, P.J., 1990. Aspects of the behavioural ecology of inshoredolphins Tursiops truncatus and Sousa chinensis in Moreton Bay,Australia. In: Leatherwood, S., Reeves, R.R. (Eds.), TheBottlenose Dolphin. Academic Press, London, pp. 285–293.

Corkeron, P.J., Morissette, N.M., Porter, L.J., Marsh, H., 1997.Distribution and status of hump-backed dolphins, Sousachinensis, in Australian waters. Asian Marine Biology 14,49–59.

DeMaster, D.P., Fowler, C.W., Perry, S.L., Richlen, M.E., 2001.Predation and competition: the impact of fisheries onmarine-mammal populations over the next one hundredyears. Journal of Mammalogy 82, 641–651.


Forney, K.A., 2000. Environmental models of cetacean abundance:reducing uncertainty in population trends. ConservationBiology 14, 1271–1286.

Gerrodette, T., 1987. A power analysis for detecting trends.Ecology 68, 1364–1372.

Gribble, N.A., McPherson, G., Lane, B., 1998. Effect of theQueensland Shark Control Program on non-target species:whale, dugong, turtle and dolphin: a review. Marine andFreshwater Research 49, 645–651.

Hastie, G.D., Barton, T.R., Grellier, K., Hammond, P.S., Swift, R.J.,Thompson, P.M., Wilson, B., 2003. Distribution of smallcetaceans within a candidate Special Area of Conservation:implications for management. Journal of Cetacean Researchand Management 5, 261–266.

Haynes, J., Limpus, C.J., 2000. Marine wildlife stranding andmortality database annual report, 2000: II. Cetaceans andpinnipeds. Research Coordination Unit, Parks and WildlifeStrategy Division, Queensland Parks and Wildlife Service,Brisbane.

Haynes, J., Limpus, C.J., 2002. Marine wildlife stranding andmortality database annual report 2001: II. Cetacean andpinniped. Wildlife Ecology Unit, Environmental SciencesDivision, Queensland Environmental Protection Agency,Brisbane.

Haynes, J., Limpus, C.J., Flakus, S., 1999. Marine wildlife strandingand mortality database annual report, 1999: II. Cetacean andpinnipeds. Research Coordination Unit, Planning andResearch Division, Queensland Parks and Wildlife Service,Brisbane.

Heinsohn, G.E., 1979. Biology of small cetaceans in northQueensland Waters. The Great Barrier Reef Marine ParkAuthority, Townsville, Queensland.

Hooker, S.K., Whitehead, H., Gowans, S., 1999. Marine protectedarea design and the spatial and temporal distribution ofcetaceans in a submarine canyon. Conservation Biology 13,592–602.

Ingram, S.N., Rogan, E., 2002. Identifying critical areas and habitatpreferences of bottlenose dolphinsTursiops truncatus. MarineEcology Progress Series 244, 247–255.

Irons, D.B., 1998. Foraging area fidelity of individual seabirds inrelation to tidal cycles and flock feeding. Ecology 79, 647–655.

Jefferson, T.A., 2004. Geographic variation in skull morphology ofhumpback dolphins (Sousa spp.). Aquatic Mammals 30, 3–17.

Jefferson, T.A., Karczmarski, L., 2001. Sousa chinensis. MammalianSpecies 655, 1–9.

Kendall, W.L., 1997. Estimating temporary emigration usingcapture–recapture data with Pollock�s robust design. Ecology78, 2248.

King, G., 2003. The Townsville region: a social atlas. Communityand Cultural Services Department, Townsville City Council,Townsville.

Kochman, H.I., O�Shea, T.J., Deutsch, C.J., Reid, J.P., Bonde, R.K.,Easton, D.E., 2003. Seasonal movements, migratory behavior,and site fidelity of West Indian manatees along the Atlanticcoast of the United States. Wildlife Monographs (151), 1–77.

Lebreton, J.-D., Burnham, K.P., Clobert, J., Anderson, D.R., 1992.Modeling survival and testing biological hypotheses usingmarked animals: a unified approach with case studies.Ecological Monographs 62, 67–118.

Levine, N., 2002. CrimeStat II: a spatial statistics program for theanalysis of crime incident locations. Ned Levine andAssociates, Houston, TX, and the National Institute of JusticeWashington, DC.

Limpus, C.J., Haynes, J., Currie, K.J., 2003. Marine wildlifestranding and mortality database annual report 2002: II.Cetacean and pinniped. Environmental Sciences Unit,Environmental and Technical Science Division, QueenslandEnvironmental Protection Agency, Brisbane.

Martin, A.R., DaSilva, V.M.F., 1998. Tracking aquatic vertebrates indense tropical forest using VHF telemetry. Marine TechnologySociety Journal 32, 82–88.

McIntyre, A.D., 1999. Conservation in the sea: looking ahead.Aquatic Conservation: Marine and Freshwater Ecosystems 9,633–637.

Moore, P.G., 1999. Fisheries exploitation and marine habitatconservation: a strategy for rational coexistence. AquaticConservation: Marine and Freshwater Ecosystems 9, 585–591.

Parra, G.J., 2005. Behavioural ecology of Irrawaddy, Orcaellabrevirostris (Owen in Gray, 1866), and Indo-Pacific humpbackdolphins, Sousa chinensis (Osbeck, 1765), in northeastQueensland, Australia: a comparative study. Ph.D. Thesis,James Cook University, Townsville.

Parra, G.J., Corkeron, P.J., 2001. Feasibility of usingphoto-identification techniques to study the Irrawaddydolphin, Orcaella brevirostris (Owen in Gray 1866). AquaticMammals 27, 45–49.

Parra, G.J., Azuma, C., Preen, A.R., Corkeron, P.J., Marsh, H., 2002.Distribution of Irrawaddy dolphins, Orcaella brevirostris, inAustralian waters. Raffles Bulletin of Zoology Supplement 10,141–154.

Parra, G.J., Corkeron, P.J., Marsh, H., 2004. The Indo-Pacifichumpback dolphin, Sousa chinensis (Osbeck, 1765), inAustralian waters: a summary of current knowledge. AquaticMammals 30, 197–206.

Paterson, R.A., 1990. Effects of long-term anti-shark measures ontarget and non-target species in Queensland Australia.Biological Conservation 52, 147–159.

Pollock, K.H., Nichols, J.D., Brownie, C., Hines, J.E., 1990. StatisticalInference for capture–recapture experiments. WildlifeMonographs 107, 1–98.

Pradel, R., 1993. Flexibility in survival analysis from recapturedata: handling trap-dependence. In: Lebreton, J.D., North, P.M.(Eds.), Marked Individuals in the Study of Bird Populations.Birkhauser Verlag, Basel, Switzerland, pp. 29–37.

Preen, A., Marsh, H., 1995. Response of dugongs to large-scale lossof seagrass from Hervey Bay, Queensland, Australia. WildlifeResearch 22, 507–519.

Read, A.J., Urian, K.W., Wilson, B., Waples, D.M., 2003. Abundanceof bottlenose dolphins in the bays, sounds, and estuaries ofNorth Carolina. Marine Mammal Science 19, 59–73.

Reed, D.H., O�Grady, J.J., Brook, B.W., Ballou, J.D., Frankham, R.,2003. Estimates of minimum viable population sizes forvertebrates and factors influencing those estimates. BiologicalConservation 113, 23–34.

Robertson, A.I., Duke, N.C., 1987. Mangroves as nursery sites:comparisons of the abundance and species composition offish and crustaceans in mangroves and other nearshorehabitats in tropical Australia. Marine Biology 96, 193–205.

Robertson, A.I., Duke, N.C., 1990. Mangrove fish-communities intropical Queensland, Australia: spatial and temporal patternsin densities biomass and community structure. MarineBiology 104, 369–379.

Schwarz, C.J., Arnason, A.N., 1996. A general methodology for theanalysis of capture–recapture experiments in openpopulations. Biometrics 52, 860–873.

Smith, B.D., Beasley, I., Kreb, D., 2003. Marked declines inpopulations of Irrawaddy dolphins. Oryx 37, 401.

Switzer, P.V., 1997a. Factors affecting site fidelity in a territorialanimal, Perithemis tenera. Animal Behaviour 53, 865–877.

Switzer, P.V., 1997b. Past reproductive success affects futurehabitatselection. Behavioral Ecology and Sociobiology 40, 307–312.

Taylor, B.L., Gerrodette, T., 1993. The uses of statistical power inconservation biology: the vaquita and northern spotted owl.Conservation Biology 7, 489–500.

Thompson, P.M., Wilson, B., Grellier, K., Hammond, P.S., 2000.Combining power analysis and population viability analysis to


compare traditional and precautionary approaches toconservation of coastal cetaceans. Conservation Biology 14,1253–1263.

Warkentin, I.G., Hernandez, D., 1996. The conservationimplications of site fidelity: a case study involvingnearctic-neotropical migrant songbirds wintering in a CostaRican mangrove. Biological Conservation 77, 143–150.

Whitehead, H., 2001. Analysis of animal movement usingopportunistic individual identifications: application to spermwhales. Ecology 82, 1417–1432.

Whitehead, H., 2004. Programs for the analysis of animal socialstructure (SOCPROG 2.0.).

Williams, J.A, Dawson, S.M., Slooten, E., 1993. The abundance anddistribution of bottlenosed dolphins (Tursiops truncatus) in

Doubtful Sound, New Zealand. Canadian Journal of Zoology71, 2080–2088.

Williams, B.K., Nichols, J.D., Conroy, M.J., 2002. Analysis andManagement of Animal Populations: Modeling, Estimation,and Decision Making. Academic Press, San Diego, California.

Wilson, B., Hammond, P.S., Thompson, P.M., 1999. Estimating sizeand assessing trends in a coastal bottlenose dolphinpopulation. Ecological Applications 9, 288–300.

Wursig, B., Jefferson, T.A., 1990. Methods of photo-identificationfor small cetaceans. In Individual Recognition of Cetaceans:Use of Photo-Identification and Other Techniques to EstimatePopulation Parameters, eds. P.S. Hammond, S.A. Mizroch, G.P.Donovan, pp. 43–52, Special issue 12. International WhalingCommission, Cambridge.

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